Green and blue infrastructure design in a semi-arid region
Bibliographic record
Abstract
The Green and Blue Infrastructure (GBI) is a new approach for dealing with biodiversity conservation based on maintaining the ecological networks by ensuring the survival of animal and plant species. GBI is considered a new public policy to avoid the loss of biodiversity by preserving landscape connectivity and protecting natural habitats from fragmentation. Countries including the United States, Canada, the United Kingdom, Italy, Portugal, Japan, China, Brazil, and Turkey are seeking to integrate this concept into their environmental policies. This study proposed a methodology to design a GBI at a regional scale applied to the Sousse region in Tunisia. Two main approaches were used. The first was based on identifying three reference species marked by different dispersal distances. The second applied GIS spatial analysis using the buffer model to help assess the ecological connectivity. The methodology developed comprises five steps. In the first step, a forest habitat sub-frame, a semi-open habitat sub-frame, and a wetland sub-frame were determined from a detailed land-use map. Then, different biodiversity reservoirs were identified. In the third step, buffers varying between 200, 250, and 300 m depending on the dispersal distance of the reference species (Cape hare, North African Hedgehog, and Marbled Teal) were applied around the reservoirs to identify zones of potential connections between them. Next, buffers were also applied to detect fragmentation zones along road networks and built-up areas. Finally, the ecological corridors were mapped from the overlay between zones of potential connections and the fragmentation map. The result was the creation of a GBI of the Sousse region with reservoirs of biodiversity and ecological corridors connecting them. This methodological approach and the characterization of the Sousse city GBI map represent the first innovative research applied in Tunisia. This prototype can be a reference for other local or regional spaces, contributing toward realizing a national GBI and promoting sustainable projects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".